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Chinese NER problem that needs to capture 18 types of entities in medical conversation text. The process is divided into 4 parts that are encapsulated in high-level abstract classes. We control the workflow in a single Jupyter notebook.
This repository contains the social media data scraper and the notebooks of this analysis. Where we analise the Social Media posts - tweets with Sentiment Analysis then we analyse this results with Named Entity Recognition (NER) and Information Extraction methods to get a more accurate and detailed picture of this sentiment results.